The Experts below are selected from a list of 64239 Experts worldwide ranked by ideXlab platform
Richard M Leahy - One of the best experts on this subject based on the ideXlab platform.
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brainsuite an automated cortical surface identification Tool
Medical Image Analysis, 2002Co-Authors: David W Shattuck, Richard M LeahyAbstract:Abstract We describe a new magnetic resonance (MR) Image Analysis Tool that produces cortical surface representations with spherical topology from MR Images of the human brain. The Tool provides a sequence of low-level operations in a single package that can produce accurate brain segmentations in clinical time. The Tools include skull and scalp removal, Image nonuniformity compensation, voxel-based tissue classification, topological correction, rendering, and editing functions. The collection of Tools is designed to require minimal user interaction to produce cortical representations. In this paper we describe the theory of each stage of the cortical surface identification process. We then present classification validation results using real and phantom data. We also present a study of interoperator variability.
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brainsuite an automated cortical surface identification Tool
Medical Image Computing and Computer-Assisted Intervention, 2000Co-Authors: David W Shattuck, Richard M LeahyAbstract:We describe a new magnetic resonance (MR) Image Analysis Tool that produces cortical surface representations with spherical topology from MR Images of the human brain. The Tool provides a sequence of low-level operations in a single package that can produce accurate brain segmentations in clinical time. The Tools include skull and scalp removal, Image nonuniformity compensation, voxel-based tissue classification, topological correction, rendering, and editing functions. The collection of Tools is designed to require minimal user interaction to produce cortical representations. In this paper we describe the theory of each stage of the cortical surface identification process. We then present validation results using real and phantom data.
Alain Thorel - One of the best experts on this subject based on the ideXlab platform.
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a new approach to characterize the nanostructure of activated carbons from mathematical morphology applied to high resolution transmission electron microscopy Images
Carbon, 2013Co-Authors: Pascaline Pre, Guillaume Huchet, Dominique Jeulin, J N Rouzaud, Mohamed Sennour, Alain ThorelAbstract:Abstract A new characterization method of the nanoporous structure of activated carbons (ACs) is proposed, based on mathematical morphology Analysis of high resolution transmission electron microscopy (TEM) Images. It produces refined statistics describing the shape, size and orientation of the defective graphene sheets seen edge on as individual fringes on TEM Images. It also provides some quantitative information regarding their spatial arrangement. Especially, assemblages composed of 2–4 nearly parallel fringe fragments could be detected, which were relevant of some partial stacking of the defective graphene sheets. Such assemblages were possibly locally oriented along a common direction to form large continuous domains. To prove the ability of the Image Analysis Tool to reveal distinctive features and degrees of disorder of the AC structures, a set of various commercial carbon adsorbents was characterized. The measured effective spaces separating the individual fringes, the stacks and the continuous domains were examined and compared with the porosity data derived from 77 K–N2 adsorption isotherms. Consistency between the two sets of data was assessed and interpreted by considering the N2 diffusional limitations resulting from the micropore network connectivity.
Abigail R Gerhold - One of the best experts on this subject based on the ideXlab platform.
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centtracker a trainable machine learning based Tool for large scale analyses of c elegans germline stem cell mitosis
Molecular Biology of the Cell, 2021Co-Authors: Reda M Zellag, Yifan Zhao, Vincent Poupart, Ramya Singh, Jeanclaude Labbe, Abigail R GerholdAbstract:Investigating the complex interactions between stem cells and their native environment requires an efficient means to Image them in situ. Caenorhabditis elegans germline stem cells (GSCs) are distinctly accessible for intravital imaging; however, long-term Image acquisition and Analysis of dividing GSCs can be technically challenging. Here we present a systematic investigation into the technical factors impacting GSC physiology during live imaging and provide an optimized method for monitoring GSC mitosis under minimally disruptive conditions. We describe CentTracker, an automated and generalizable Image Analysis Tool that uses machine learning to pair mitotic centrosomes and which can extract a variety of mitotic parameters rapidly from large-scale datasets. We employ CentTracker to assess a range of mitotic features in a large GSC data set. We observe spatial clustering of mitoses within the germline tissue, but no evidence that subpopulations with distinct mitotic profiles exist within the stem cell pool. We further find biases in GSC spindle orientation relative to the germline's distal-proximal axis, and thus the niche. The technical and analytical Tools provided herein pave the way for large-scale screening studies of multiple mitotic processes in GSCs dividing in situ, in an intact tissue, in a living animal, under seemingly physiological conditions.
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centtracker a trainable machine learning based Tool for large scale analyses of c elegans germline stem cell mitosis
bioRxiv, 2020Co-Authors: M R Zellag, Vincent Poupart, Ramya Singh, Jeanclaude Labbe, Ying Zhao, Abigail R GerholdAbstract:Abstract Investigating the complex interactions between stem cells and their native environment requires an efficient means to Image them in situ. Caenorhabditis elegans germline stem cells (GSCs) are distinctly accessible for intravital imaging; however, long-term Image acquisition and Analysis of dividing GSCs can be technically challenging. Here we present a systematic investigation into the technical factors impacting GSC physiology during live imaging and provide an optimized method for monitoring GSC mitosis under minimally disruptive conditions. We describe CentTracker, an automated and generalizable Image Analysis Tool that uses machine learning to pair mitotic centrosomes and which can extract a variety of mitotic parameters rapidly from large-scale datasets. We employ CentTracker to assess a range of mitotic features in GSCs and show that subpopulations with distinct mitotic profiles are unlikely to exist within the stem cell pool. We further find evidence for spatial clustering of GSC mitoses within the germline tissue and for biases in mitotic spindle orientation relative to the germline’s distal-proximal axis, and thus the niche. The technical and analytical Tools provided herein pave the way for large-scale screening studies of multiple mitotic processes in GSCs dividing in situ, in an intact tissue, in a living animal, under seemingly physiological conditions.
David W Shattuck - One of the best experts on this subject based on the ideXlab platform.
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brainsuite an automated cortical surface identification Tool
Medical Image Analysis, 2002Co-Authors: David W Shattuck, Richard M LeahyAbstract:Abstract We describe a new magnetic resonance (MR) Image Analysis Tool that produces cortical surface representations with spherical topology from MR Images of the human brain. The Tool provides a sequence of low-level operations in a single package that can produce accurate brain segmentations in clinical time. The Tools include skull and scalp removal, Image nonuniformity compensation, voxel-based tissue classification, topological correction, rendering, and editing functions. The collection of Tools is designed to require minimal user interaction to produce cortical representations. In this paper we describe the theory of each stage of the cortical surface identification process. We then present classification validation results using real and phantom data. We also present a study of interoperator variability.
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brainsuite an automated cortical surface identification Tool
Medical Image Computing and Computer-Assisted Intervention, 2000Co-Authors: David W Shattuck, Richard M LeahyAbstract:We describe a new magnetic resonance (MR) Image Analysis Tool that produces cortical surface representations with spherical topology from MR Images of the human brain. The Tool provides a sequence of low-level operations in a single package that can produce accurate brain segmentations in clinical time. The Tools include skull and scalp removal, Image nonuniformity compensation, voxel-based tissue classification, topological correction, rendering, and editing functions. The collection of Tools is designed to require minimal user interaction to produce cortical representations. In this paper we describe the theory of each stage of the cortical surface identification process. We then present validation results using real and phantom data.
Yifan Zhao - One of the best experts on this subject based on the ideXlab platform.
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centtracker a trainable machine learning based Tool for large scale analyses of c elegans germline stem cell mitosis
Molecular Biology of the Cell, 2021Co-Authors: Reda M Zellag, Yifan Zhao, Vincent Poupart, Ramya Singh, Jeanclaude Labbe, Abigail R GerholdAbstract:Investigating the complex interactions between stem cells and their native environment requires an efficient means to Image them in situ. Caenorhabditis elegans germline stem cells (GSCs) are distinctly accessible for intravital imaging; however, long-term Image acquisition and Analysis of dividing GSCs can be technically challenging. Here we present a systematic investigation into the technical factors impacting GSC physiology during live imaging and provide an optimized method for monitoring GSC mitosis under minimally disruptive conditions. We describe CentTracker, an automated and generalizable Image Analysis Tool that uses machine learning to pair mitotic centrosomes and which can extract a variety of mitotic parameters rapidly from large-scale datasets. We employ CentTracker to assess a range of mitotic features in a large GSC data set. We observe spatial clustering of mitoses within the germline tissue, but no evidence that subpopulations with distinct mitotic profiles exist within the stem cell pool. We further find biases in GSC spindle orientation relative to the germline's distal-proximal axis, and thus the niche. The technical and analytical Tools provided herein pave the way for large-scale screening studies of multiple mitotic processes in GSCs dividing in situ, in an intact tissue, in a living animal, under seemingly physiological conditions.
